Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jan 15, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

LeqMod:适应性损伤-量化-对深度学习进行一致的调制-低数PET图像去除.

Menghua Xia, Huidong Xie, Qiong Liu

    IEEE transactions on medical imaging
    |October 6, 2025
    PubMed
    概括
    此摘要是机器生成的。

    相关概念视频

    您也可能阅读

    相关文章

    通过共同作者、期刊和引用图与本文相关的文章。

    排序
    Same author

    Multimodal neuroimaging elucidates the posterior-to-anterior gradient of nigrostriatal degeneration in Parkinson's disease.

    NPJ Parkinson's disease·2026
    Same author

    Targeting the crosstalk between Alzheimer's disease and gastrointestinal cancers.

    Molecular medicine (Cambridge, Mass.)·2026
    Same author

    Detailed pharmacokinetic features of a novel bis-boron <sup>18</sup>F-trifluoroborate acid in healthy volunteers: comparable and additional values of total-body PET-imaging-derived analysis.

    European journal of nuclear medicine and molecular imaging·2026
    Same author

    Outcome-driven dosimetry optimization for [<sup>177</sup>Lu]Lu-PSMA-617 radiopharmaceutical therapy: proof of concept on single time point dosimetry optimization.

    European journal of nuclear medicine and molecular imaging·2026
    Same author

    Impact of scan duration and scatter correction on quantitative accuracy in Yttrium-90 PET/CT imaging using LAFOV and SAFOV systems: a phantom and clinical evaluation.

    EJNMMI physics·2026
    Same author

    Visual analysis of [<sup>18</sup>F]FDG PET/CT imaging yields superior diagnostic accuracy compared to semiquantitative analysis in the assessment of giant cell arteritis.

    European journal of nuclear medicine and molecular imaging·2026
    Same journal

    UniOCTSeg++: Refined Hierarchical Prompt Strategy and Bi-directional Progressive Consistency Learning for Universal Retinal Layer Segmentation in OCT.

    IEEE transactions on medical imaging·2026
    Same journal

    Volumetric Functional Ultrasound Imaging in Macaques.

    IEEE transactions on medical imaging·2026
    Same journal

    MUST: Multi-style virtual staining with incomplete pairs.

    IEEE transactions on medical imaging·2026
    Same journal

    BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

    IEEE transactions on medical imaging·2026
    Same journal

    LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

    IEEE transactions on medical imaging·2026
    Same journal

    Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

    IEEE transactions on medical imaging·2026
    查看所有相关文章

    一个新的深度学习策略,LeqMod,通过提高病变可见性和量化准确性来增强正子发射断层扫描 (PET) 图像无色化. 这种方法可以减少辐射暴露,同时保持医学成像中的关键细节.

    科学领域:

    • 医疗成像医学成像
    • 人工智能的人工智能
    • 放射学 放射学是一门学科.

    背景情况:

    • 基于深度学习的正电子发射断层扫描 (PET) 图像无色化旨在减少辐射暴露和扫描时间.
    • 当前的方法往往会模糊重要的细节,从而对病变量化准确性产生负面影响.
    • 准确的量化对于在PET成像中有效的诊断和治疗监测至关重要.

    研究的目的:

    • 引入一种新的策略,LeqMod (损伤感知和量化一致的调制),用于增强PET图像消噪.
    • 改进无色化PET图像中的病变可见性和量化一致性.
    • 开发一种可适应各种深度学习架构的插即用方法,而不会增加推断计算负载.

    主要方法:

    • LeqMod策略将下游病变量化分析作为模型训练中的辅助工具.
    • 它包括两个组成部分:LeMod (损伤感知调制) 和QuMod (多尺度量化一致调制).
    • LeMod使用不同的采样重量和损失标准,用于病变存在的样本与没有病变的样本,由辅助细分网络指导.
    • QuMod的重点是提高标准化吸收值 (SUVmean和SUVmax) 在多个尺度和子区域的准确性.

    主要成果:

    • 对大型,多中心,多供应商PET数据集的实验证明了LeqMod在不同的Denoising框架中的有效性.

    更多相关视频

    Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
    05:32

    Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

    Published on: February 21, 2025

    669

    相关实验视频

    Last Updated: Jan 15, 2026

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.3K
    Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
    05:32

    Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

    Published on: February 21, 2025

    669
  • 与非LeqMod框架相比,LeqMod的整合减少了5.92%的平均病变SUVmax偏差.
  • 在参与站点中,LeqMod 提高了0.36的平均峰值信号噪声比 (PSNR).

    • 结论:

    • 拟议的LeqMod策略通过保留关键细节和提高量化准确性,显著提高PET图像无色化.
    • LeqMod提供了一种多功能且计算效率高的解决方案,用于提高低数量的PET图像的质量和诊断效用.
    • 这种方法有望减少辐射剂量和扫描时间,同时在临床PET成像中保持高诊断性能.